{"id":10179,"date":"2026-08-17T21:25:37","date_gmt":"2026-08-17T13:25:37","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10179"},"modified":"2026-08-17T23:57:10","modified_gmt":"2026-08-17T15:57:10","slug":"jase-202611-34-048","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-048","title":{"rendered":"Cross-Modal Retrieval and Personalized Recommendation for Integrated Innovation, Entrepreneurship, and Moral Education"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Zijin Li and Weijie Zhao<\/i><\/a><\/p>\n\n\n\n

Pingdingshan University, Pingdingshan Henan 467000, China<\/p>\n<\/div>\n\n\n\n

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Received: May 04, 2026
Accepted: July 20, 2026
Publication Date: August 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Overall architecture of the integrated teaching platform <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.048<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In the context of the knowledge economy and the transformation of higher education, cultivating innovation, entrepreneurship, and moral responsibility has become a strategic priority. Existing innovation and entrepreneurship education platforms often fail to address diverse student profiles and rarely integrate moral education into personalized learning pathways. This study proposes an integrated teaching platform that unifies innovation, entrepreneurship, and moral education through multimedia networks and neural network-driven cross-modal semantic retrieval. The platform consists of a student learning space, teacher management modules, a multimedia resource repository, and real-time feedback mechanisms. A hybrid neural network model is introduced to map multimodal educational resources and student submissions into a shared semantic space using weak semantic label generation, bidirectional feature crossing, attention-enhanced GRU modules, and position encoding. In this framework, weak semantic labels are automatically generated pseudo-labels derived from semantic category probability distributions of unlabeled multimodal samples and are iteratively incorporated into model training to enhance semantic representation learning. This framework enables personalized content recommendation, dynamic interest tracking, and moral dilemma feedback. Experiments conducted on the Wikipedia, Wikipedia-CNN, NUS-WIDE, and domain-specific I&E-EDU datasets demonstrate that the proposed method consistently outperforms baseline approaches, including MRCR-SNN. On the I&E-EDU dataset, the proposed model improves mean Average Precision (mAP) from 28.3% to 32.7% (+4.4 percentage points), Precision@10 from 43.1% to 49.4% (+6.3 percentage points), and increases student engagement by 26.5%.<\/p>\n\n\n\n

Keywords: Cross-modal retrieval; Personalized recommendation; Entrepreneurship education; Moral education; Multimodal neural networks.<\/em><\/p>\n\n\n\n

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